Decision time for clinical decision support systems

Clinical decision support systems are interactive software systems designed to assist clinicians with decision making tasks, such as determining a diagnosis or recommending a treatment for a patient. Clinical decision support systems are a widely researched topic in the Computer Science community but their inner workings are less well understood by and known to clinicians. In this article we provide a brief explanation of clinical decision support systems and provide some examples of real world systems. We also describe some of the challenges to implementing these systems in clinical environments and posit some of the reasons for limited adoption of decision support systems in practice. We aim to engage clinicians in the development of decision support system that can meaningfully help with their decision making tasks and open up a discussion about the future of automated clinical decision support as a part of healthcare delivery

A Means to what End? Evaluating the Explainability of Software Systems using Goal-Oriented Heuristics

Explainability is an emerging quality aspect of software systems. Explanations offer a solution approach for achieving a variety of quality goals, such as transparency and user satisfaction. Therefore, explainability should be considered a means to an end. The evaluation of quality aspects is essential for successful software development. Evaluating explainability allows an assessment of the quality of explanations and enables the comparison of different explanation variants. As the evaluation depends on what quality goals the explanations are supposed to achieve, evaluating explainability is non-trivial. To address this problem, we combine the already well-established method of expert evaluation with goal-oriented heuristics. Goal-oriented heuristics are heuristics that are grouped with respect to the goals that the explanations are meant to achieve. By establishing appropriate goal-oriented heuristics, software engineers are enabled to evaluate explanations and identify problems with affordable resources. To show that this way of evaluating explainability is suitable, we conducted an interactive user study, using a high-fidelity software prototype. The results suggest that the alignment of heuristics with specific goals can enable an effective assessment of explainability

Multimedia Explanations in IDEA Decision Support Systems

In this paper, we present a new approach to support the decision of selecting one object out of a set of alternatives. As compared to previous approaches, the distinctive feature of our approach is that neither the user, nor the system need to build a model of user's preferences. Our proposal is to integrate a system for interactive data exploration and analysis with a multimedia explanation facility. The explanation facility supports the user in understanding unexpected aspects of the data. The explanation generation process is guided by a causal model of the domain that is automatically acquired by the system. Introduction With the rapid increase in the amount of on-line, up-to-date information, more and more people, ranging from professional public-policy decision makers to common people, will base their decisions on on-line sources. Thus, there is an increasing need for software systems that support interactive, information-intensive decision making for different user populations ..

The KB paradigm and its application to interactive configuration

The knowledge base paradigm aims to express domain knowledge in a rich formal language, and to use this domain knowledge as a knowledge base to solve various problems and tasks that arise in the domain by applying multiple forms of inference. As such, the paradigm applies a strict separation of concerns between information and problem solving. In this paper, we analyze the principles and feasibility of the knowledge base paradigm in the context of an important class of applications: interactive configuration problems. In interactive configuration problems, a configuration of interrelated objects under constraints is searched, where the system assists the user in reaching an intended configuration. It is widely recognized in industry that good software solutions for these problems are very difficult to develop. We investigate such problems from the perspective of the KB paradigm. We show that multiple functionalities in this domain can be achieved by applying different forms of logical inferences on a formal specification of the configuration domain. We report on a proof of concept of this approach in a real-life application with a banking company. To appear in Theory and Practice of Logic Programming (TPLP).Comment: To appear in Theory and Practice of Logic Programming (TPLP

The Grammar of Interactive Explanatory Model Analysis

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory interpretations of the same phenomenon. Surprisingly, the majority of methods developed for explainable machine learning focus on a single aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper presents how different Explanatory Model Analysis (EMA) methods complement each other and why it is essential to juxtapose them together. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe potential human-model dialogues. IEMA is implemented in the human-centered framework that adopts interactivity, customizability and automation as its main traits. Combined, these methods enhance the responsible approach to predictive modeling.Comment: 17 pages, 10 figures, 3 table

Automated Reasoning and Presentation Support for Formalizing Mathematics in Mizar

This paper presents a combination of several automated reasoning and proof presentation tools with the Mizar system for formalization of mathematics. The combination forms an online service called MizAR, similar to the SystemOnTPTP service for first-order automated reasoning. The main differences to SystemOnTPTP are the use of the Mizar language that is oriented towards human mathematicians (rather than the pure first-order logic used in SystemOnTPTP), and setting the service in the context of the large Mizar Mathematical Library of previous theorems,definitions, and proofs (rather than the isolated problems that are solved in SystemOnTPTP). These differences poses new challenges and new opportunities for automated reasoning and for proof presentation tools. This paper describes the overall structure of MizAR, and presents the automated reasoning systems and proof presentation tools that are combined to make MizAR a useful mathematical service.Comment: To appear in 10th International Conference on. Artificial Intelligence and Symbolic Computation AISC 201

Ten Simple Rules for Reproducible Research in Jupyter Notebooks

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific progress. Since many experimental studies rely on computational analyses, biologists need guidance on how to set up and document reproducible data analyses or simulations. In this paper, we address several questions about reproducibility. For example, what are the technical and non-technical barriers to reproducible computational studies? What opportunities and challenges do computational notebooks offer to overcome some of these barriers? What tools are available and how can they be used effectively? We have developed a set of rules to serve as a guide to scientists with a specific focus on computational notebook systems, such as Jupyter Notebooks, which have become a tool of choice for many applications. Notebooks combine detailed workflows with narrative text and visualization of results. Combined with software repositories and open source licensing, notebooks are powerful tools for transparent, collaborative, reproducible, and reusable data analyses

Development and evaluation of a multimedia interactive CD: Public speaking interactive media

This paper reports on a study that endeavours to develop a Computer Assisted Learning (CAL) multimedia courseware namely, Public Speaking Interactive Media. This courseware was developed specifically for diploma students undergoing ENG4113 (Business English) and ENG 4153 (Public Speaking Skills) at Kolej Profesional MARA Indera Mahkota, Kuantan, Pahang. The objectives and goals of this study is to develop a CAL courseware which is in-line with the syllabus of the courses using multimedia elements together with the application of behaviorist, cognitive and constructivist learning theories as a basis in the design of the courseware. Moreover, the instructional design and implementation of this CAL multimedia courseware employ active and flexible learning strategies. Utilizing Hannafin and Peck’s Design Model, this courseware was developed using Macromedia Director and Macromedia Authorware to ensure that multimedia elements and simulations can be fully integrated. The findings of the study revealed that the courseware fulfilled its objectives in aiding students in comprehending the concept of public speaking skills better by using multimedia elements. In addition, the courseware is in-line with the syllabus and has incorporated the theories and strategies intended successfully